Bike as Best Friend: A Multimodal Crowdsourced Framework for Anticipating Cycling Conditions in Greater Copenhagen Area

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Original abstract

Crowdsourcing is emerging as a powerful form of civic participation in sustainable mobility planning. Citizen engagement is growing in Northern European countries such as Denmark, renowned for its focus on inclusive and sustainable transport modes, particularly cycling. In this context, municipalities increasingly rely on crowdsourced platforms to collect reports on missing infrastructure or damages (e.g., potholes in cycle paths) affecting cyclists’ safety and satisfaction. This study proposes a multimodal crowdsourced framework to assess and anticipate cycling conditions at the municipal scale, focusing on Gladsaxe Municipality in the Greater Copenhagen Area. The framework combines citizen-generated reports (text and images) from the Giv et Praj platform with official indicators from the National Bicycle Account to explore interactions between perceived and actual cycling conditions. Crowdsourced content is analysed using artificial intelligence techniques for semantic and visual classification, identifying recurring issues related to accessibility, comfort, infrastructure, and safety. These signals are integrated into a Perceived Cycling Conditions Index (PCCI), capturing spatial and temporal variations in perceived cycling conditions. PCCI is compared with official indicators including modal share, user satisfaction, and accident statistics, through directional consistency analysis and cross-indicator ranking. Using data for 2023–2024, the framework is validated and extended to an indicative transition analysis, providing early qualitative signals of potential changes in 2025. Results highlight the value of crowdsourced and AI-supported analytics as anticipatory decision-support tools, enabling local authorities to detect emerging needs, prioritize interventions, and support more inclusive and resilient cycling systems.

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